We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today’s environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications.

Hydrogen is acknowledged as a potential and appealing energy carrier for decarbonizing the sectors that contribute to global warming, such as power generation, industries, and transportation. Many people are interested in employing low-carbon sources of energy to produce hydrogen by using water electrolysis. Additionally, the intermittency of renewable energy supplies, such as wind and solar, makes electricity generation less predictable, potentially leading to power network incompatibilities. Hence, hydrogen generation and storage can offer a solution by enhancing system flexibility. Hydrogen saved as compressed gas could be turned back into energy or utilized as a feedstock for manufacturing, building heating, and automobile fuel. This work identified many hydrogen production strategies, storage methods, and energy management strategies in the hybrid microgrid (HMG). This paper discusses a case study of a HMG system that uses hydrogen as one of the main energy sources together with a solar panel and wind turbine (WT). The bidirectional AC-DC converter (BAC) is designed for HMGs to maintain power and voltage balance between the DC and AC grids. This study offers a control approach based on an analysis of the BAC’s main circuit that not only accomplishes the function of bidirectional power conversion, but also facilitates smooth renewable energy integration. While implementing the hydrogen-based HMG, the developed control technique reduces the reactive power in linear and non-linear (NL) loads by 90.3% and 89.4%.

Cervical cancer is one of the common cancers among women and it causes significant mortality in many developing countries. Diagnosis of cervical lesions is done using pap smear test or visual inspection using acetic acid (staining). Digital colposcopy, an inexpensive methodology, provides painless and efficient screening results. Therefore, automating cervical cancer screening using colposcopy images will be highly useful in saving many lives. Nowadays, many automation techniques using computer vision and machine learning in cervical screening gained attention, paving the way for diagnosing cervical cancer. However, most of the methods rely entirely on the annotation of cervical spotting and segmentation. This paper aims to introduce the Faster Small-Object Detection Neural Networks (FSOD-GAN) to address the cervical screening and diagnosis of cervical cancer and the type of cancer using digital colposcopy images. The proposed approach automatically detects the cervical spot using Faster Region-Based Convolutional Neural Network (FR-CNN) and performs the hierarchical multiclass classification of three types of cervical cancer lesions. Experimentation was done with colposcopy data collected from available open sources consisting of 1,993 patients with three cervical categories, and the proposed approach shows 99% accuracy in diagnosing the stages of cervical cancer.